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CJS 2023, 10(1): 54-66 |
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Azimi M H, Jozi Z. Scientometrics and analysis of thematic clusters of research in the field of ontology in information retrieval. CJS 2023; 10 (1) :54-66
URL: http://cjs.mubabol.ac.ir/article-1-305-en.html
URL: http://cjs.mubabol.ac.ir/article-1-305-en.html
Department of Knowledge and Information Science, Faculty of Education and Psychology, Shahid Chamran University of Ahvaz, Ahvaz, Iran , z-jozi@stu.scu.ac.ir
Abstract: (2871 Views)
Background and aim: The combination of ontology-based retrieval systems leads to the semantic retrieval of words. The aim of this study was to review ontology articles in information retrieval using scientometric techniques.
Materials and methods: The present study was conducted using the documentary method and word cluster analysis. The research population comprised 2595 articles indexed in two databases, Scopus and Web of Science, from 2001 to 2023. The data were analyzed using Excel, BibExcel, SPSS 26 and UCINET. VOSviewer was used to draw research maps.
Findings: The growth of articles in ontology and information retrieval was low and the annual growth rate averaged 0.11%.Stanford and California universities were the most prolific organizations, contributing to 6 articles, and the field of computer science was the most prolific with 43% of the articles written. The word clustering led to the formation of 4 thematic clusters: semantic retrieval of information, non-human ontology, classification of systems, and role of technology. In addition, there was a positive correlation between science production and centralities (degree centrality 0.323, closeness centrality 0.278, and betweenness centrality 0.447).
Conclusion: The evolution of the words used in the articles has shown that although the growth of article production in this field has increased from the beginning, the development of ontology technologies in information retrieval started with a weak semantic system called information classification, and after the various stages of development, it now uses machine learning to understand user requirements and process information with the help of artificial intelligence.
Materials and methods: The present study was conducted using the documentary method and word cluster analysis. The research population comprised 2595 articles indexed in two databases, Scopus and Web of Science, from 2001 to 2023. The data were analyzed using Excel, BibExcel, SPSS 26 and UCINET. VOSviewer was used to draw research maps.
Findings: The growth of articles in ontology and information retrieval was low and the annual growth rate averaged 0.11%.Stanford and California universities were the most prolific organizations, contributing to 6 articles, and the field of computer science was the most prolific with 43% of the articles written. The word clustering led to the formation of 4 thematic clusters: semantic retrieval of information, non-human ontology, classification of systems, and role of technology. In addition, there was a positive correlation between science production and centralities (degree centrality 0.323, closeness centrality 0.278, and betweenness centrality 0.447).
Conclusion: The evolution of the words used in the articles has shown that although the growth of article production in this field has increased from the beginning, the development of ontology technologies in information retrieval started with a weak semantic system called information classification, and after the various stages of development, it now uses machine learning to understand user requirements and process information with the help of artificial intelligence.
Keywords: Ontology, Information retrieval, Knowledge retrieval, Scientometrics, Clustering, Word co-occurrence
Type of Study: Orginal |
Subject:
Scientometrics
Received: 2023/07/12 | Revised: 2023/11/21 | Accepted: 2023/11/11 | ePublished: 2023/11/21
Received: 2023/07/12 | Revised: 2023/11/21 | Accepted: 2023/11/11 | ePublished: 2023/11/21
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